Data-driven Sliding-mode Control for Bridge Cranes Against Limited Communication and Data Dropouts

This paper investigates a data-driven sliding-mode control issue for bridge cranes with limited communication and data dropouts. Firstly, the nonlinear model of the bridge crane is partially feedback linearized, where adaptive signals are introduced. A fast terminal sliding mode surface is constructed based on the transformed system model, and a data-driven sliding mode control method is designed. Then, an event-triggering mechanism is developed to reduce communication burdens. In addition, a comprehensive compensation strategy is designed to solve the data dropouts problem. Finally, the convergence of the control error governed by the proposed method is rigorously proved, and simulation studies verify the proposed method’s effectiveness.